Cognition models for wireless communication systems and method and apparatus for optimal utilization of a radio channel based on cognition model data

ABSTRACT

Classes of cognition models which may include: 1) Radio Environment models, 2) Mobility models and 3) Application/User Context models are utilized in a wireless communications network. Radio Environment models represent the physical aspects of the radio environment, such as shadowing losses, multi-path propagation, interference and noise levels, etc. Mobility models represent users motion, in terms of geo-coordinates and/or logical identifiers, such as street names etc. as well as speed of user terminal etc. The context model represents the present state and dynamics of each of these application processes within itself and between multiple application processes. These data are employed to optimize network performance.

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority from U.S. provisional application No. 60/337,241 and filed on Nov. 30, 2001 which is incorporated by reference as if fully set forth.

FIELD OF THE INVENTION

[0002] The present invention relates to wireless communications. More particularly, the present invention relates to cognitive radio and the employment of multiple classes of cognitive radio modelers in wireless communications and method and apparatus making optimal use of the radio channel based on information from the cognitive modelers.

BACKGROUND

[0003] Cognitive Radio involves three layers: cognition models, a language for communicating the descriptors of the cognition models and a processor for analyzing cognition descriptors and making decisions. The invention encompasses three classes of cognition models.

SUMMARY OF THE INVENTION

[0004] Three classes of cognition models are proposed as follows:

[0005] 1) Radio Environment models,

[0006] 2) Mobility models and

[0007] 3) Application/User Context models.

[0008] Radio environment represents the physical aspects, mobility predicts the future positions of a user terminal while the application represents the present state and dynamics of each of these application processes within itself and between multiple application processes.

BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The invention will be understood from the following description and drawings in which like elements are designated by like numerals and, wherein:

[0010]FIG. 1 is a block diagram of a user equipment (UE) embodiment of the present invention;

[0011]FIG. 2 is a block diagram of a network embodiment of the present invention;

[0012]FIG. 3 outlines the physical model attributes of the present invention;

[0013]FIG. 4 depicts the mobility modeler attributes of the present invention; and

[0014]FIG. 5 describes the application context models of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] The invention proposes three classes of cognition models:

[0016] 1) Radio Environment models,

[0017] 2) Mobility models and

[0018] 3) Application/User Context models.

[0019] Radio Environment models represent the physical aspects of the radio environment, such as shadowing losses, multi-path propagation, interference, noise levels, etc.

[0020] Mobility models represent users motion, in terms of geo-coordinates and/or logical identifiers, such as street names etc. as well as speed of movement of user terminals, etc. The Mobility models are used for predicting future positions of the user terminal.

[0021] Application/User Context represents the application environment that the user is presently in and can be used for predicting the application environment in future time instants. For example, an application context may consist of a user browsing the web using http/tcp/hip protocols, user talking on a voice call, involved in a file transfer, such as music download, etc.

[0022] The context model represents the present state and dynamics of each of these application processes per se and between multiple application processes. For example, Internet browsing is often modeled in terms of packet session, packet calls, number, duration and separation of individual packets etc. This represents the context modeling within an application process. The context modeling between multiple application processes consists of representing the dynamics of how users may move from one application process to another, etc.

[0023]FIGS. 1 and 2 respectively show embodiments of a user equipment (UE) and a network, based on the principles of cognitive radio and the three types of cognition models described above.

[0024]FIG. 1 is an embodiment employing three types of cognition models, while FIG. 2 is an example of cognitive radio network, employing three types of cognition models and a cognitive radio resource manager. Tables 1 (FIG. 2), 2 (FIG. 3) and 3 (FIG. 4) detail the attributes of the physical, mobility and context modelers respectively. The three parts are independent. Systems can be built using one or more of the three classes of models.

[0025]FIG. 1 shows the user UE 10 comprising a geographic data base 12, a physical modeler 14 and a mobility modeler 16. The geographic database stores geo-locations and location related attributes for the geo-locations which may include land formations, such as hills, mountains, etc., buildings, trees, atmospheric attributes, etc. The physical modeler 14 provides radio related attributes such as multi-path attributes, shadowing attributes and Doppler attributes associated with the geographic locations.

[0026] The mobility modeler 16 provides information associated with UEs such as their geo-coordinates, velocity, road topology along which UEs may be traveling including traffic lights, etc. and traffic density. This data is transferred to the channel processor 18 which prepares the data for transmission to modem 26, i.e. maps the application data to the channel and identifies received data and directs the received data to the proper destination. The data, at baseband, is modulated with an appropriate radio frequency at 28 and transmitted through antenna apparatus 30 for communication with the network.

[0027] The applications which may include internet browsing, speech activity e-mail, instant messaging, etc. are provided to the application context modeler 22 and application processor 24 for modeling. For example, internet browsing is often modeled in terms of packet session, packet calls, number, duration and separation of individual packets, etc. This data is provided to the channel processor 18 for subsequent transmission, in the manner described hereinabove with regard to mobility and physical modelers 14 and 16, respectively. The various applications handled by application circuitry 20, are shown in FIG. 5. The application processor 24 incorporates the coding and processing for forwarding data to the proper destination, for example, providing the necessary coding and processing for internet browsing (TCP/IP), voice communication, images, short message service (SMS); and multimedia service (MMS).

[0028]FIG. 2 shows a network unit, wherein like elements are designated by like numerals and further including a cognitive radio resource controller (RRC) respectively coupled to the application context, physical and mobility modelers 22, 14, and 16. The RRC 32 normally controls optimal transmission of packets over the air and further manages spectral resources to ensure that quality of service (QoS) is maintained. User traffic and radio channel performance is routinely monitored for purposes of controlling air interface parameters. Air bandwidth allocation and revenue maximization are controlled, together with carrier policies, to assure QoS is judiciously applied to generate revenue based on usage charges, subscription, or other subscriber policies. The RRC utilizes information from the modelers 14, 16 and 22 to make more efficient use of the radio channel.

[0029] Typically, the physical modeler 14 makes a number of measurements of the radio channel. For example, physical modeler 14 measures the interference levels and/or noise levels; measures the channel impulse response; and estimates the multipath characteristics. These characteristics include the total energy, the delay spread, the number of significant paths (also called ‘fingers’) and the locations of these significant paths; Doppler shifts; the large scale path losses, etc. The art of these measurements is well established in the literature. In addition, the physical modeler 14 may also determine the location of one or more UEs. When the modeler 14 is implemented in the UE, then it may determine its own location, whereas if it is implemented in the Network, it may determine the locations of more than one UE. The UE may determine its own location by a global positioning system (GPS), not shown for purposes of simplicity, or Network assisted GPS. The Network may determine the locations of UEs employing Base Station triangulation principles.

[0030] The location information may be related to a local geographic map and related to roads, intersections, landmarks, buildings, hills, parks, etc. Based on such relations, the physical radio environment may be characterized as being indoor, dense urban, urban, rural, hilly, highway etc. These measurements form the parameters of the physical modeler 14.

[0031] Similarly, the mobility modeler 16 estimates the future locations of the UE or UEs in relation to a geographic map. For instance, if the UE is located on a highway and is moving at a certain velocity, then its future positions can be estimated. In case the UE is located near an intersection in a downtown area, then the road information will provide several alternatives for the future locations with associated probabilities. The set of possible future positions of a UE, together with associated probabilities become the parameters of the mobility modeler 16.

[0032] Finally, the application context is modeled. Depending upon the specific application the user is engaged in, the current and future data rate and QoS requirements can be estimated. For example, assuming the user (UE) is engaged in a voice conversation, then the amount of data generated can be modeled based on general speech characteristics and the voice compression algorithm currently being used. Similarly, if the user is engaged in a web browsing session, the packet flows can be modeled in a statistical fashion. For example, web browsing is typically performed employing TCP/IP protocol, which has a certain structure. As an example, the TCP session is always preceded by a 3-way handshake, involving small amounts of data transfer. This is typically followed by a number of request-response type transactions. The request messages are small in size, whereas the response can be much larger. Similarly, email applications, file transfer protocol (FTP) applications, short message system (SMS) applications, multimedia system (MMS) applications, picture messaging applications, etc. can be characterized by the protocol structure and data statistics. These characteristics form the parameters of the application context modeler 22.

[0033] The various modelers can be implemented in the UE and/or the Network. The network and optionally the UE also implements a so-called cognitive controller, shown as a radio resource controller 32, which accepts the parameters from modelers 14, 16 and 22 as inputs and processes them for determining optimal radio performance. Specifically, the cognitive controller (RRC) 32 determines optimal data rates, error correction coding schemes, antenna beam widths, power levels, application queue dimensions, etc. The current radio parameters are accordingly adjusted. In some cases, new processes may be invoked, such as the turning on or off of acknowledged mode of radio data transmission. In such cases, radio parameters are either selected or aborted. The cognitive controller (RRC) 32 in the UE and in the network may be input with local cognition model parameters, as in the case of local optimization in a UE or the network. The cognitive controller (RRC) in the network may also be input with local cognition model parameters as well as cognition model parameters of various UEs, which have been transmitted to the network. In this case, each UE uses one or more of the radio channels and reports the cognition model parameter data. A suitable set of messages and reporting structure is used for the protocol. The network then processes the local as well as remote (i.e., from the UEs) cognition model data and generates various adjustments for optimal or improved performance. While some of these adjustments are affected locally in the network, the others would be transmitted to the concerned UE, using appropriate command protocols. This results in a cognitive radio system, which strives to perform optimally in changing physical, user and application conditions by using the data generated by the various cognition models. 

What is claimed is:
 1. A method for modeling a physical radio environment about a base station (BS) to enhance wireless communications comprising: obtaining location data from a geographic data base; and obtaining data from a physical attributes data base associated with the location data provided by the geographic data base.
 2. The method of claim 1 wherein the step of obtaining location data further includes obtaining attributes related to a physical location.
 3. The method of claim 2 wherein the step of obtaining attributes further includes obtaining data relating to land formations such as hills, mountains, bodies of water, trees, manmade objects such as buildings, bridges and atmospheric attributes associated with the location data.
 4. The method of claim 1 wherein the step of obtaining physical attributes associated with location data further includes modeling multipath characteristics.
 5. The method of claim 4 wherein modeling multipath characteristics includes modeling delay spread energy, number of significant paths and their locations expressed as numbers or ranges and energies of the paths, large scale fading or shadowing loss and doppler shift.
 6. A method for modeling a physical radio environment about a user equipment (UE) to enhance wireless communications, comprising: obtaining location data from a geographic data base; and obtaining data from a physical attributes data base associated with the location data obtained from the geographic data base.
 7. The method of claim 6 wherein the step of obtaining location data further includes obtaining attributes related to the physical location data.
 8. The method of claim 7 wherein the step of obtaining attributes further includes obtaining land formations such as hills, mountains, bodies of water, trees, manmade objects such as buildings, bridges and atmospheric attributes associated with the location data.
 9. The method of claim 2 wherein the step of obtaining attributes associated with location data further includes modeling multipath characteristics.
 10. The method of claim 9 wherein modeling multipath characteristics includes modeling delay spread energy, number of significant paths and their locations expressed as numbers or ranges and energies of the paths, large scale fading or shadowing loss and doppler shift.
 11. A method for modeling a physical environment of a radio path between a base station (BS) and a user equipment (UE) to enhance wireless communications, comprising: said BS obtaining location data from a geographic data base related to a region incorporating said radio path; and said BS obtaining, from a physical attributes data base, physical attributes associated with said region.
 12. The method of claim 11 wherein the step of obtaining location data further includes obtaining attributes related to said region.
 13. The method of claim 12 wherein obtaining attributes further includes obtaining land formations such as hills, mountains, bodies of water, trees, manmade objects such as buildings, bridges and atmospheric attributes of said region.
 14. The method of claim 1 further comprising: obtaining radio related attributes associated with said region.
 15. The method of claim 14 wherein obtaining radio related attributes includes modeling multipath characteristics.
 16. The method of claim 15 wherein modeling multipath characteristics includes modeling delay spread energy, number of significant paths and their locations expressed as numbers or ranges and energies of the paths, large scale fading or shadowing loss and doppler shift.
 17. A method for modeling a physical radio environment about a base station to enhance wireless communications, comprising: obtaining location data of a user equipment (UE) from a geographic data base; and obtaining mobility information associated with a UE based on location data from the geographic data base.
 18. The method of claim 17 wherein obtaining mobility information includes obtaining geo-coordinates, velocity, road topology along which the UE may be traveling including traffic lights and other traffic signals and signs and traffic density.
 19. The method of claim 17 wherein obtaining location data further includes obtaining attributes related to a physical location.
 20. The method of claim 19 wherein obtaining attributes further includes obtaining data regarding land formations such as hills, mountains, bodies of water, trees, manmade objects such as buildings, bridges and atmospheric attributes associated with the UE.
 21. The method of claim 19 wherein obtaining attributes includes obtaining radio related attributes associated with the location data, including modeling multipath characteristics such as delay spread energy, number of significant paths and their locations expressed as numbers or ranges and energies of the paths, large scale fading or shadowing loss and doppler shift.
 22. A method for modeling a physical radio environment about a user equipment (UE) to enhance wireless communications, comprising: obtaining location data of a user equipment (UE) from a geographic data base; and obtaining mobility information associated with the UE based on location data from the geographic data base.
 23. The method of claim 22 wherein obtaining attributes includes obtaining radio related attributes associated with the location data, including modeling multipath characteristics such as delay spread energy, number of significant paths and their locations expressed as numbers or ranges and energies of the paths, large scale fading or shadowing loss and doppler shift.
 24. A method for modeling application context at a base station, to enhance wireless communication, comprising: examining an application currently in use; and modeling application context based on the current usage.
 25. The method of claim 24 wherein the step of modeling application context includes modeling a current data rate and quality of service (QoS) requirements.
 26. The method of claim 24 wherein the step of modeling an application context further includes providing an estimated future data rate, and estimated quality of service (QoS) requirements.
 27. The method of claim 24 wherein modeling application context includes modeling support for handling the application currently in use.
 28. The method of claim 24 wherein the examining step includes determining if the current application is one of voice communication, web browsing, email, FTP, SMS, MMS, image transmissions and messaging.
 29. The method of claim 28 wherein the application context includes modeling the amount of data generated based on speech characteristics and a voice compression algorithm currently in use when the current application is voice communication.
 30. The method of claim 27 wherein modeling support for the current application includes providing coding and processing to support the current application.
 31. The method of claim 24 further comprising selecting communication parameters responsive to the detected application.
 32. A wireless network comprising: a base station (BS) and a plurality of user equipments (UEs), the base station having a local modeler for modeling radio environment in a vicinity of the base station; the plurality of user equipments (UEs) each having a radio environment modeler for modeling radio environment in the vicinity of each respective UE; each of said UEs transmitting environment model data obtained locally to the base station; and said BS having a cognitive controller for adjusting and/or selecting network operational parameters and/or UE operational parameters based on the environmental models obtained locally and from the UEs.
 33. The network of claim 32 wherein selecting parameters includes selecting/adjusting one or more of transmitted power, coding methods, data rates, ARQ parameters, radiation pattern including beam shape, call admission policies, congestion control policies and queuing policies.
 34. The network of claim 32 wherein each UE has a mobility modeler for modeling mobility attributes for use by the BS.
 35. The network of claim 34 wherein each mobility modeler models at least one of road or street topology, velocity and traffic density.
 36. The network of claim 32 wherein each UE further includes a context modeler for modeling context for use by the BS.
 37. The network of claim 32 wherein the BS has a mobility modeler for modeling mobility attributes for use by the BS.
 38. The network of claim 32 wherein the BS further includes a context modeler for modeling context for use by the BS.
 39. The network of claim 37 wherein each UE includes means for sending at least one of data modeled by its associated UE.
 40. The network of claim 39 wherein the BS includes means for receiving modeled data from said UEs.
 41. The network of claim 32 wherein the BS includes means for analyzing modeled data from the local BS modelers and the UE modelers and means for sending adjustment/selection data to at least one of said UEs responsive to the analyzing means.
 42. The network of claim 32 wherein the BS includes means for analyzing modeled data from the local BS modelers and the UE modelers and means for adjusting transmission parameters responsive to the analyzing means.
 43. Apparatus for use by a network for wireless communication with at least one user equipment (UE) comprising: a physical modeler for providing data regarding physical characteristics located between the network and the at least one UE; and means for selecting and/or adjusting the techniques employed for transmitting information to the at least one UE over a physical radio environment in accordance with data provided by the physical modeler.
 44. The apparatus of claim 43, comprising: a mobility modeler which takes into account location velocity, road topology and traffic density of a UE for adjusting information transmitted to the at least one UE.
 45. The apparatus of claim 43, comprising: an application/user context modeler containing data including the application environment of the at least one user equipment and for predicting application environment in future time instance adjust information transmitted to the UE.
 46. A wireless communication network having a base station (BS) and at least one user equipment (UE) comprising: said BS having a physical modeler for controlling communication in accordance with physical characteristics located between the BS and the at least one UE; and transmitting information to the at least one UE which has been adjusted in accordance with the physical characteristics encountered.
 47. The network of claim 46 further comprising: a mobility modeler which takes into account location, velocity, road topology and traffic density of a UE for adjusting information transmitted to the at least one UE.
 48. The network of claim 45 further comprising: an application/user context modeler containing data including the application environment of the at least one UE and means for predicting application environment in future time to adjust information transmitted to the UE. 